Accelerated primal-dual gradient method for smooth and convex-concave saddle-point problems with bilinear coupling
D Kovalev, A Gasnikov… - Advances in Neural …, 2022 - proceedings.neurips.cc
In this paper we study the convex-concave saddle-point problem $\min_x\max_y f (x)+
y^\top\mathbf {A} xg (y) $, where $ f (x) $ and $ g (y) $ are smooth and convex functions. We …
y^\top\mathbf {A} xg (y) $, where $ f (x) $ and $ g (y) $ are smooth and convex functions. We …
Lower bounds and optimal algorithms for smooth and strongly convex decentralized optimization over time-varying networks
We consider the task of minimizing the sum of smooth and strongly convex functions stored
in a decentralized manner across the nodes of a communication network whose links are …
in a decentralized manner across the nodes of a communication network whose links are …
RandProx: Primal-dual optimization algorithms with randomized proximal updates
L Condat, P Richtárik - arXiv preprint arXiv:2207.12891, 2022 - arxiv.org
Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization
problems, in particular those arising in machine learning. We propose a new primal-dual …
problems, in particular those arising in machine learning. We propose a new primal-dual …
DADAO: Decoupled accelerated decentralized asynchronous optimization
This work introduces DADAO: the first decentralized, accelerated, asynchronous, primal, first-
order algorithm to minimize a sum of $ L $-smooth and $\mu $-strongly convex functions …
order algorithm to minimize a sum of $ L $-smooth and $\mu $-strongly convex functions …
Accelerated primal-dual methods for linearly constrained convex optimization problems
H Luo - arXiv preprint arXiv:2109.12604, 2021 - arxiv.org
This work proposes an accelerated primal-dual dynamical system for affine constrained
convex optimization and presents a class of primal-dual methods with nonergodic …
convex optimization and presents a class of primal-dual methods with nonergodic …
Dualize, split, randomize: Toward fast nonsmooth optimization algorithms
We consider minimizing the sum of three convex functions, where the first one F is smooth,
the second one is nonsmooth and proximable and the third one is the composition of a …
the second one is nonsmooth and proximable and the third one is the composition of a …
Continuous-time analysis of anchor acceleration
Recently, the anchor acceleration, an acceleration mechanism distinct from Nesterov's, has
been discovered for minimax optimization and fixed-point problems, but its mechanism is not …
been discovered for minimax optimization and fixed-point problems, but its mechanism is not …
Distributed proximal splitting algorithms with rates and acceleration
We analyze several generic proximal splitting algorithms well suited for large-scale convex
nonsmooth optimization. We derive sublinear and linear convergence results with new rates …
nonsmooth optimization. We derive sublinear and linear convergence results with new rates …
Accelerating value iteration with anchoring
Value Iteration (VI) is foundational to the theory and practice of modern reinforcement
learning, and it is known to converge at a $\mathcal {O}(\gamma^ k) $-rate. Surprisingly …
learning, and it is known to converge at a $\mathcal {O}(\gamma^ k) $-rate. Surprisingly …
Stochastic proximal point methods for monotone inclusions under expected similarity
Monotone inclusions have a wide range of applications, including minimization, saddle-
point, and equilibria problems. We introduce new stochastic algorithms, with or without …
point, and equilibria problems. We introduce new stochastic algorithms, with or without …